Data can be presented and interpreted and re-interpreted and over interpreted. The details and parameters are often skimmed over and missed. The devil is in the detail. It helps to be cynical and nit picky. Pedanticism only exists as a concept for those who don't appreciate the details and subtleties therin. People so often simply see what they want to see and remain happy in their self imposed echo chamber.
My bottom line is that we have a personal responsibility to think for ourselves. To be as healthy as we can be - by ourselves and for our own self. Then the whole data thing is interesting when viewed as an outsider. I have come to realise that the data we are given is not just open to interpretation, it is massively open to fraud. We are presented all these figures and charts and numbers and statistics which we have to take at face value. But what of them are actually real or as they initially appear? Governments all over the world have stopped publishing and/or updating data. Data accumulated in sites such as ourworldindata take information as given by the governments. So - what is real, what is hidden, unknown, unassessed and misinterpreted?
Who knows. I don't. Even when I think I know I still keep questioning. The answers will either confirm or enlighten... So sad that so many people grow out of the 'why?' phase of life at such an early stage.
"But if this is generalizable, then it means that they also became infected at the highest rate just before the study begins, from mid-June to mid-September. Which means they are heavily represented in the BA.4/5 group, which experiences the least infections from mid-September to mid-December. Which also means, that in the next three months, the heavily-boosted who were infected during the study period will be the least infected."
I don't think so. Fall infection rates were about 2% in the no-dose group, 3.3% in the 1-dose group, 4.6% in the 2-dose group, 5.5% in the 3 dose group and 6% in the > 3 dose group. All are well under 10% and vary by at most a factor of 3. There were more infections in the summer. If we assume the ratios between groups didn't vary that tells us rates were about 3% in the no-dosers and 10% in the >3 dosers. Removing 10% of the >3 dosers from the eligibility pool (or even the probability of infection pool) due to recent infection still leaves 90% to get infected in the fall. It's a simple matter to see that the same stable infection rates could be maintained from season to season due to low-enough infection rates to minimize the number of recently infected and therefore more immune.
The same paper reports an bivalent booster efficacy of 30%. They are not at all clear about the reference point for the bivalent RR calculation. Using the already elevated rates for 2, 3 or 3+ doses is a great way to cheat. But let's just assume 30% to be generous. Is this relative to the That suggests a window of perhaps 60 to at most 120 days of some degree of vaccine efficacy in preventing at least a detected infection. This suggests that perhaps there was still summer efficacy for some of the >3 dosers who were vaccinated in the spring, but probably 1-3 months earlier relative to the risk window start date than the bivalent boosted were for the fall. So perhaps there is a bit of catch up, but I would argue most of this is higher infection rates in those with lots of doses, especially among those with no dose within about the past 90 days.
Over on twitter some Australian politician wondered if the mRNA shots are actually driving the production of variants because they probably have errors in their mRNA and they don't have perfect copy fidelity: https://twitter.com/SenatorRennick/status/1609884867839553537
At first I thought that must be rubbish, but then I got to wondering if someone who was boosted several times and has had their b-cells class switch to producing IgG4 antibodies it seems possible that in some cells they might be churning out virions that have Spike generated from defective mRNA ...
Of course, who knows how many of those defective Spike proteins would actually allow the virions to function of even if this is possible.
Well, the variants are defined by their sequences so even if they are wearing transfection-coded spike we wouldn't know. And conversely if they somehow picked up the transfection RNA it would lead to 100s of SNPs showing in the sequence, because of all the codon optimization.
Dec 31, 2022·edited Dec 31, 2022Liked by Brian Mowrey
I'm late to this but this is a great description from a professor on infectious disease in S. Korea, admitting that a low level of natural immunity led to a sharp rise in Omicron, despite the third highest adult vaxx rate in the world at 97%
There's a fair degree of face saving in there but far more honesty than you'd get from mainstream health experts back here in the west.
Looks obvious in hindsight but the best protection from infection with Omicron is... infection from Omicron. All the mandates and unnecessary vaxxing only serve to keep the pandemic going.
It's interesting that cases in the UK are a small fraction of a few months ago but deaths are about the same; an inconvenient truth that must be ignored by the political and health leadership along with the media.
Anyway have the best New Year Brian and thanks for all your hard work! 🎆🎉🥳
Thanks for this trollish study challenge and the opportunity to tactilely participate in option selection! I appreciated the quiz was not too hard.
I am still wary of studies based on covid infections because of the unknown accuracy of PCR testing. This study tested for nucleic acid which in my understanding uses pcr. And it presumed that most people had symptoms before testing since the Cleveland clinic didn't require asymptomatic testing. I'm more confident in hospitalization and death data, and most confident in death data, even though that has been wonky too. I don't want to forget that infection data seems to be the least reliable.
Regarding PCR, Shrestha's previous natural immunity study has long been the best argument for PCR accuracy; and this study only adds to it. If most PCR's were "false positives," it would be impossible that the most recently false-positive would be the least false-positive in the subsequent 3 months, as found in this study.
Instead, people who are previously or recently PCR-positive are always the ones who are least PCR-positive going forward. This would be incredibly, incredibly difficult to explain under any model that doesn't associate PCR-positivity with an experience that grants protection against later PCR-positivity by an understood biological basis (ie, infection + immunity).
"So all that the study is showing is that the highly-boosted are still catching up with natural immunity."
Maybe, Brian. I mean, while your interpretation fits the released data, the released data do not prove your interpretation. If you would just replace the first "is" with "may be" in the sentence above, I would have to agree (despite the siren song of groupthink).
However, what the study does NOT show is that continual boosting does anything whatsoever to prevent the spread of COVID on population level.
Other recent studies, and the overall prevalence of the illness, especially in highly vaccinated nations like Japan, present further evidence against continual boosting on population level.
I know from personal experience that getting COVID while unvaccinated sucks. But unlike those hiding in fear with 5+ mRNA doses who still got (or will get) COVID in the end anyway, I did my part for the greater "herd immunity" of my society.
"what the study does NOT show is that continual boosting does anything whatsoever to prevent the spread of COVID on population level."
Right - but since my entire argument is that the apparent "real-time" negative efficacy of uber-boosting is an artifact of "catching up" with natural immunity, it does not include a claim that boosters reduce overall case rates long term. It is effectively a claim of net futility. I have always been against boosters as being totally pointless and stupid long term; but it precisely because of this cynical attitude that I do not find the Cleveland Clinic study surprising or impressive.
I understood your point, and it was a good point to make.
I am still waiting for a single (even somewhat) well designed epidemiological study that compares the overall health outcomes of totally unvaccinated populations to the overall health outcomes of demographically comparable vaccinated and boosted populations. We are now two years into this mass experiment, and the most important study that any public health analyst actually interested in the overall benefit vs. cost profile of these mRNA injections would have published is still yet to be published.
Am I missing something? Do you know of any such study or any study that even comes close to addressing the most critical question, which is whether these injections (from day 1, not day 14 after the second dose) actually do more good than harm?
This is generally provided by the ONS figures for all-cause mortality by dose state, but the huge fluctuations here are a good example, again, of how categorizing people can create distortions. So when you create a category that equates to "got some doses, but then stopped getting any more" you inadvertently select for people who had some type of health downturn and die in high numbers, as in my post from May (https://unglossed.substack.com/p/the-panera-kingdom-problem).
You could "fix" this problem by force-combining the deaths and person-years for all the injected together so you are only comparing the unvaccinated with the vaccinated regardless of dose-count; but that doesn't change that the unvaccinated group *also* included people with a recent health downturn, who were close to death, to begin with. At least in the UK where vax uptake was nearly universal in adults.
So you're better off with logic and biology. Experimental gene therapy poison = bad.
Right, but that was just hacking the already published trial numbers, which are the product of huge data abnormalities in terms of "excluding" hundreds of participants after injection.
I got it! the group that experienced the most infections was the group that experienced the most infections!
I haven't looked at this study, but noting that the large black line are those with no prior infection should raise some eyebrows as to how to interpret this information.
I suppose this is an example of the need for critical thinking, or really why it's so important that we teach people HOW to think more than WHAT. What would just tell us that the jabbed are getting infected the most, while HOW would tell us to take a careful look at how the information is stratified and what exactly the graph is measuring.
I'm actually curious of what you think of the whole IgG4 thing going on right now. I wrote a post but there's so much to discuss with it and I haven't formed all of my thoughts, but I looked at the supplemental data and there's some interesting information, including one person whose IgG4 ratio appeared to decline over time which...is weird? Not sure, this is all novel and we need more information.
Ab ratios can fluctuate since all B Cell lineages can expand and/or contract. So copies of IgG4 Memory B Cells might be pruned down after recent antigen encounter. Generally the ratios of expanded and non-expanded Ab / B Cell IgG phenotypes will mirror each other since encounter also drives differentiation of lineages but there is room for plasticity on this front.
That's interesting. I'm far too naiive in immunology so I'd have to look into this further and see if my use of that figure would be proper then.
Also, I forgot to mention that I saw someone use the figure above to argue ADE, but this was in a post arguing about IgG4, and I was confused given that ADE is inherently a consequence of effector function, but with effector cells acting like little boy scouts helping the old pathogen cross the cellular membrane.
So I'm completely confused as to how we can throw ADE and IgG4 together since they should be completely contradictory concepts. If so, I think this is a growing example of how ideas are just being thrown out without being properly pieced together.
As MD alludes to, this is because the BA.4/5 infected are not considered "at risk" until they hit 90 days from their BA.4/5 infection, which 0 of them have reached in week 1. So they parachute into the risk pool in the subsequent weeks.
Dec 30, 2022·edited Dec 30, 2022Liked by Brian Mowrey
This sort of gets to Brian's point in some regard. Remember that the black line indicates people who haven't had any previous infection, so of course as these people become infected they will move over to the "infected group". So the numbers essentially state that people are becoming infected.
Edit: Not other groups, the Ba.4/Ba.5 group in particular as that is the circulating variant at the time looked at in this study.
You'll note that the orange line says "last infected with Omicron Ba.4/Ba.5", and that number is empty because no one in the cohort was infected with Ba.4/Ba.5 yet at the beginning of the study. However, as time goes on more people become infected with this variant (ba.4/ba.5) shuffling them over to the orange group because of course the last variant these people would be infected with would be the circulating variant.
I knew it. I KNEW it. Actually the original one made people more susceptible to the virus too. Barely anyone under 60 was getting sick prior to the vaccine, but afterward it seemed like everyone was.
This is probably "because variants," although I remain of two minds whether the VOCs are products of natural evolution or just fresh "infusions" of lab-edited virus, which leads them to reflect different exposure conditions. Either way, Alpha coincides with the vaccine rollout but it isn't caused by it (it emerges in September).
The recently-injected do not actually get infected that much, at least before Omicron (when anecdotal evidence at least has been strong). Instead, absent even anecdotal support, a lot of substack writers have just assumed a "worry window" is somehow hiding in the data for the 20/21 winter wave, the same way physicists assume "dark matter" is real because otherwise they would have to rewrite equations. It doesn't mean it's real.
Liar!!! Just kidding. But overall my impression is that anecdotes of proximate post-dose infection were rare until this summer, when they have become rather common. Again as I mentioned in the comments to the last post (I think), I say this as someone with a second-hand post-3rd-dose-infection anecdote which was the source of my Omicron exposure last January. So it happened in my circle but I still get the impression it is rare. You have things like the Bar-On booster series where the simple choice to get boosted assigns a weird halo of protection for several days.
And in general, as my argument about the Cleveland Clinic study goes, this summer is when a lot of the uber-boosted finally had to face reality with the virus and that is why we are now seeing the tolerance-induced after-effects of infection in this group.
Very interesting.
Data can be presented and interpreted and re-interpreted and over interpreted. The details and parameters are often skimmed over and missed. The devil is in the detail. It helps to be cynical and nit picky. Pedanticism only exists as a concept for those who don't appreciate the details and subtleties therin. People so often simply see what they want to see and remain happy in their self imposed echo chamber.
My bottom line is that we have a personal responsibility to think for ourselves. To be as healthy as we can be - by ourselves and for our own self. Then the whole data thing is interesting when viewed as an outsider. I have come to realise that the data we are given is not just open to interpretation, it is massively open to fraud. We are presented all these figures and charts and numbers and statistics which we have to take at face value. But what of them are actually real or as they initially appear? Governments all over the world have stopped publishing and/or updating data. Data accumulated in sites such as ourworldindata take information as given by the governments. So - what is real, what is hidden, unknown, unassessed and misinterpreted?
Who knows. I don't. Even when I think I know I still keep questioning. The answers will either confirm or enlighten... So sad that so many people grow out of the 'why?' phase of life at such an early stage.
Whoops, a bit of a ramble there.
Thanks for posting this Brian, it's good work.
Pinned, as a gesture. [My comment sections are hardly so busy as to make pinning significant.]
"But if this is generalizable, then it means that they also became infected at the highest rate just before the study begins, from mid-June to mid-September. Which means they are heavily represented in the BA.4/5 group, which experiences the least infections from mid-September to mid-December. Which also means, that in the next three months, the heavily-boosted who were infected during the study period will be the least infected."
I don't think so. Fall infection rates were about 2% in the no-dose group, 3.3% in the 1-dose group, 4.6% in the 2-dose group, 5.5% in the 3 dose group and 6% in the > 3 dose group. All are well under 10% and vary by at most a factor of 3. There were more infections in the summer. If we assume the ratios between groups didn't vary that tells us rates were about 3% in the no-dosers and 10% in the >3 dosers. Removing 10% of the >3 dosers from the eligibility pool (or even the probability of infection pool) due to recent infection still leaves 90% to get infected in the fall. It's a simple matter to see that the same stable infection rates could be maintained from season to season due to low-enough infection rates to minimize the number of recently infected and therefore more immune.
The same paper reports an bivalent booster efficacy of 30%. They are not at all clear about the reference point for the bivalent RR calculation. Using the already elevated rates for 2, 3 or 3+ doses is a great way to cheat. But let's just assume 30% to be generous. Is this relative to the That suggests a window of perhaps 60 to at most 120 days of some degree of vaccine efficacy in preventing at least a detected infection. This suggests that perhaps there was still summer efficacy for some of the >3 dosers who were vaccinated in the spring, but probably 1-3 months earlier relative to the risk window start date than the bivalent boosted were for the fall. So perhaps there is a bit of catch up, but I would argue most of this is higher infection rates in those with lots of doses, especially among those with no dose within about the past 90 days.
duplicate - deleted
John Campbell discovers the Cleveland Clinic study. It is over.
Over on twitter some Australian politician wondered if the mRNA shots are actually driving the production of variants because they probably have errors in their mRNA and they don't have perfect copy fidelity: https://twitter.com/SenatorRennick/status/1609884867839553537
At first I thought that must be rubbish, but then I got to wondering if someone who was boosted several times and has had their b-cells class switch to producing IgG4 antibodies it seems possible that in some cells they might be churning out virions that have Spike generated from defective mRNA ...
Of course, who knows how many of those defective Spike proteins would actually allow the virions to function of even if this is possible.
Well, the variants are defined by their sequences so even if they are wearing transfection-coded spike we wouldn't know. And conversely if they somehow picked up the transfection RNA it would lead to 100s of SNPs showing in the sequence, because of all the codon optimization.
Ahhh, yes. I forgot that it the RNA has to copied and appear in the progeny as well as the products of the RNA.
I'm late to this but this is a great description from a professor on infectious disease in S. Korea, admitting that a low level of natural immunity led to a sharp rise in Omicron, despite the third highest adult vaxx rate in the world at 97%
https://youtu.be/fWTTSlSZC64?t=200
There's a fair degree of face saving in there but far more honesty than you'd get from mainstream health experts back here in the west.
Looks obvious in hindsight but the best protection from infection with Omicron is... infection from Omicron. All the mandates and unnecessary vaxxing only serve to keep the pandemic going.
It's interesting that cases in the UK are a small fraction of a few months ago but deaths are about the same; an inconvenient truth that must be ignored by the political and health leadership along with the media.
Anyway have the best New Year Brian and thanks for all your hard work! 🎆🎉🥳
Thanks for this trollish study challenge and the opportunity to tactilely participate in option selection! I appreciated the quiz was not too hard.
I am still wary of studies based on covid infections because of the unknown accuracy of PCR testing. This study tested for nucleic acid which in my understanding uses pcr. And it presumed that most people had symptoms before testing since the Cleveland clinic didn't require asymptomatic testing. I'm more confident in hospitalization and death data, and most confident in death data, even though that has been wonky too. I don't want to forget that infection data seems to be the least reliable.
🥂🥳 I wish you many blessings in the new year.
New years blessing-wishes for you in return! 🍾
Regarding PCR, Shrestha's previous natural immunity study has long been the best argument for PCR accuracy; and this study only adds to it. If most PCR's were "false positives," it would be impossible that the most recently false-positive would be the least false-positive in the subsequent 3 months, as found in this study.
Instead, people who are previously or recently PCR-positive are always the ones who are least PCR-positive going forward. This would be incredibly, incredibly difficult to explain under any model that doesn't associate PCR-positivity with an experience that grants protection against later PCR-positivity by an understood biological basis (ie, infection + immunity).
Okay, that gives me more confidence in PCR testing.
“A week ago (insanely; the holidays have certainly killed my productivity)...”
Holidays are SUPPOSED to kill your productivity. It’s a law! 😘
Thank you for all of your tireless work.
But bloggers are supposed to break the law, to be cool. Rebels without a pause.
Haha yes! 😂
"So all that the study is showing is that the highly-boosted are still catching up with natural immunity."
Maybe, Brian. I mean, while your interpretation fits the released data, the released data do not prove your interpretation. If you would just replace the first "is" with "may be" in the sentence above, I would have to agree (despite the siren song of groupthink).
However, what the study does NOT show is that continual boosting does anything whatsoever to prevent the spread of COVID on population level.
Other recent studies, and the overall prevalence of the illness, especially in highly vaccinated nations like Japan, present further evidence against continual boosting on population level.
I know from personal experience that getting COVID while unvaccinated sucks. But unlike those hiding in fear with 5+ mRNA doses who still got (or will get) COVID in the end anyway, I did my part for the greater "herd immunity" of my society.
"what the study does NOT show is that continual boosting does anything whatsoever to prevent the spread of COVID on population level."
Right - but since my entire argument is that the apparent "real-time" negative efficacy of uber-boosting is an artifact of "catching up" with natural immunity, it does not include a claim that boosters reduce overall case rates long term. It is effectively a claim of net futility. I have always been against boosters as being totally pointless and stupid long term; but it precisely because of this cynical attitude that I do not find the Cleveland Clinic study surprising or impressive.
I understood your point, and it was a good point to make.
I am still waiting for a single (even somewhat) well designed epidemiological study that compares the overall health outcomes of totally unvaccinated populations to the overall health outcomes of demographically comparable vaccinated and boosted populations. We are now two years into this mass experiment, and the most important study that any public health analyst actually interested in the overall benefit vs. cost profile of these mRNA injections would have published is still yet to be published.
Am I missing something? Do you know of any such study or any study that even comes close to addressing the most critical question, which is whether these injections (from day 1, not day 14 after the second dose) actually do more good than harm?
This is generally provided by the ONS figures for all-cause mortality by dose state, but the huge fluctuations here are a good example, again, of how categorizing people can create distortions. So when you create a category that equates to "got some doses, but then stopped getting any more" you inadvertently select for people who had some type of health downturn and die in high numbers, as in my post from May (https://unglossed.substack.com/p/the-panera-kingdom-problem).
You could "fix" this problem by force-combining the deaths and person-years for all the injected together so you are only comparing the unvaccinated with the vaccinated regardless of dose-count; but that doesn't change that the unvaccinated group *also* included people with a recent health downturn, who were close to death, to begin with. At least in the UK where vax uptake was nearly universal in adults.
So you're better off with logic and biology. Experimental gene therapy poison = bad.
The absence of such a study says alot to me.
I think they have done the calculations and found the overall benefit (see leaked honest government video from Australia below).
https://thumbs.gfycat.com/GrimyNeglectedDiscus-size_restricted.gif
Right, but that was just hacking the already published trial numbers, which are the product of huge data abnormalities in terms of "excluding" hundreds of participants after injection.
I got it! the group that experienced the most infections was the group that experienced the most infections!
I haven't looked at this study, but noting that the large black line are those with no prior infection should raise some eyebrows as to how to interpret this information.
I suppose this is an example of the need for critical thinking, or really why it's so important that we teach people HOW to think more than WHAT. What would just tell us that the jabbed are getting infected the most, while HOW would tell us to take a careful look at how the information is stratified and what exactly the graph is measuring.
I'm actually curious of what you think of the whole IgG4 thing going on right now. I wrote a post but there's so much to discuss with it and I haven't formed all of my thoughts, but I looked at the supplemental data and there's some interesting information, including one person whose IgG4 ratio appeared to decline over time which...is weird? Not sure, this is all novel and we need more information.
Ab ratios can fluctuate since all B Cell lineages can expand and/or contract. So copies of IgG4 Memory B Cells might be pruned down after recent antigen encounter. Generally the ratios of expanded and non-expanded Ab / B Cell IgG phenotypes will mirror each other since encounter also drives differentiation of lineages but there is room for plasticity on this front.
That's interesting. I'm far too naiive in immunology so I'd have to look into this further and see if my use of that figure would be proper then.
Also, I forgot to mention that I saw someone use the figure above to argue ADE, but this was in a post arguing about IgG4, and I was confused given that ADE is inherently a consequence of effector function, but with effector cells acting like little boy scouts helping the old pathogen cross the cellular membrane.
So I'm completely confused as to how we can throw ADE and IgG4 together since they should be completely contradictory concepts. If so, I think this is a growing example of how ideas are just being thrown out without being properly pieced together.
There's something weird going on with the number below the graph.
The first three rows are decreasing from left to right while the last two are increasing from left to right.
I guess I should read the paper to see what those numbers mean.
As MD alludes to, this is because the BA.4/5 infected are not considered "at risk" until they hit 90 days from their BA.4/5 infection, which 0 of them have reached in week 1. So they parachute into the risk pool in the subsequent weeks.
This sort of gets to Brian's point in some regard. Remember that the black line indicates people who haven't had any previous infection, so of course as these people become infected they will move over to the "infected group". So the numbers essentially state that people are becoming infected.
Edit: Not other groups, the Ba.4/Ba.5 group in particular as that is the circulating variant at the time looked at in this study.
You'll note that the orange line says "last infected with Omicron Ba.4/Ba.5", and that number is empty because no one in the cohort was infected with Ba.4/Ba.5 yet at the beginning of the study. However, as time goes on more people become infected with this variant (ba.4/ba.5) shuffling them over to the orange group because of course the last variant these people would be infected with would be the circulating variant.
Hope that helps!
I knew it. I KNEW it. Actually the original one made people more susceptible to the virus too. Barely anyone under 60 was getting sick prior to the vaccine, but afterward it seemed like everyone was.
This is probably "because variants," although I remain of two minds whether the VOCs are products of natural evolution or just fresh "infusions" of lab-edited virus, which leads them to reflect different exposure conditions. Either way, Alpha coincides with the vaccine rollout but it isn't caused by it (it emerges in September).
The recently-injected do not actually get infected that much, at least before Omicron (when anecdotal evidence at least has been strong). Instead, absent even anecdotal support, a lot of substack writers have just assumed a "worry window" is somehow hiding in the data for the 20/21 winter wave, the same way physicists assume "dark matter" is real because otherwise they would have to rewrite equations. It doesn't mean it's real.
Liar!!! Just kidding. But overall my impression is that anecdotes of proximate post-dose infection were rare until this summer, when they have become rather common. Again as I mentioned in the comments to the last post (I think), I say this as someone with a second-hand post-3rd-dose-infection anecdote which was the source of my Omicron exposure last January. So it happened in my circle but I still get the impression it is rare. You have things like the Bar-On booster series where the simple choice to get boosted assigns a weird halo of protection for several days.
And in general, as my argument about the Cleveland Clinic study goes, this summer is when a lot of the uber-boosted finally had to face reality with the virus and that is why we are now seeing the tolerance-induced after-effects of infection in this group.
So whether that's Julie Powell https://www.nytimes.com/2022/11/01/dining/julie-powell-dead.html or this ghoulish case https://www.today.com/health/coronavirus/woman-details-myocarditis-covid-symptoms-rcna63476 that is what is happening now. It's not negative infection efficacy but a kind of negative death efficacy.
Good for you! Never give in. I know people who held out for so long and then gave in. It’s tragic!